{"id":"W7115194976","doi":"10.18609/cgti.2025.013","title":"Enhancing CAR-T therapy development: harnessing cell selection flexibility","year":2025,"lang":"en","type":"article","venue":"Cell and Gene Therapy Insights","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre; Lonza (Canada)","funders":"","keywords":"Flexibility (engineering); Automation; Cellular manufacturing; Selection (genetic algorithm); Product (mathematics); Upstream (networking); Isolation (microbiology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000401163,0.000322293,0.0004323223,0.0002980681,0.0004901843,0.00009574525,0.0001301229,0.0002027063,0.0007001573],"category_scores_gemma":[0.000007857682,0.0002552219,0.0001087309,0.0005790945,0.0001169546,0.0001136949,0.00004620546,0.0003831951,0.00002382739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001818677,"about_ca_system_score_gemma":0.0005443479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007544933,"about_ca_topic_score_gemma":0.00006045195,"domain_scores_codex":[0.9980202,0.0001590423,0.0004093026,0.0006294748,0.0003347187,0.0004472896],"domain_scores_gemma":[0.9990484,0.0001050678,0.00007987537,0.0003837754,0.0002095148,0.0001733692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008852585,0.0002999138,0.002003161,0.00009965831,0.00007981136,0.000008084367,0.003017553,0.000004600047,0.8738979,0.00001179608,0.00009737458,0.1195949],"study_design_scores_gemma":[0.003293992,0.0002548467,0.00597842,0.00005424496,0.00001289408,0.000008365471,0.000154374,0.0000975661,0.8817779,0.0003042371,0.1078402,0.0002229334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663615,0.01733366,0.0008535653,0.0001483673,0.0002406731,0.0007439426,7.422219e-7,0.0001317386,0.01418587],"genre_scores_gemma":[0.9600477,0.006186847,0.001189123,0.000850104,0.0002032911,0.00005580018,0.00002369457,0.00004247625,0.03140096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.119372,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03214593148537347,"score_gpt":0.3007684147657189,"score_spread":0.2686224832803454,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}